{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "464176c0-620e-4fbe-98b3-bf02522adb6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from fog_test.for_different_strength import mix_dataset\n",
    "import os\n",
    "import cv2\n",
    "import subprocess\n",
    "from EWC_module.fisher import cal_fisher"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "75db052c-a8d6-44d3-9c74-df918ca989cc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_SI: \u001b[0mweights=./runs/train/fog_02/weights/best.pt, cfg=models/yolov5s_kitti.yaml, data=data/kitti.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=100, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=runs/train/fog_02/weights/fisher.pt, ewc_lambda=0.0005, SI_enable=False, SI_pt=None, SI_lambda=10.0\n",
      "\u001b[34m\u001b[1mgithub: \u001b[0m⚠️ YOLOv5 is out of date by 2882 commits. Use 'git pull ultralytics master' or 'git clone https://github.com/ultralytics/yolov5' to update.\n",
      "YOLOv5 🚀 cbe9b398 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/yolov5/c179ff0be2584b6fa5204027221ec893\u001b[0m\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     35061  models.yolo.Detect                      [8, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_kitti summary: 214 layers, 7041205 parameters, 7041205 gradients, 16.0 GFLOPs\n",
      "\n",
      "Transferred 348/349 items from runs/train/fog_02/weights/best.pt\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/train.cache... 4189 image\u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/val.cache... 1048 images, 0\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.81 anchors/target, 0.999 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/exp81/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/exp81\u001b[0m\n",
      "Starting training for 100 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/99      3.53G     0.0348    0.03419   0.006869        128        640: 1\n",
      "tensor([0.87743], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.669      0.378      0.432      0.248\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/99      3.53G    0.03369    0.03063   0.005304        133        640: 1\n",
      "tensor([0.92182], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.685      0.516      0.585       0.33\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/99      3.53G     0.0364    0.03287   0.006336        131        640: 1\n",
      "tensor([1.01199], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.656      0.429      0.486      0.262\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/99      3.53G    0.03795    0.03435   0.007116        108        640: 1\n",
      "tensor([0.87981], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.706      0.384      0.455      0.239\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/99      3.53G    0.03792    0.03331   0.006286        156        640: 1\n",
      "tensor([0.94480], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.668      0.556       0.61      0.337\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/99      3.53G    0.03751    0.03252   0.005775        123        640: 1\n",
      "tensor([0.87600], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.751      0.466      0.545      0.299\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/99      3.53G    0.03629    0.03159   0.005402        174        640: 1\n",
      "tensor([1.05182], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.746      0.559       0.61      0.332\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/99      3.53G    0.03613    0.03092   0.005129        166        640: 1\n",
      "tensor([1.13584], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.786      0.588      0.669      0.386\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/99      3.53G    0.03533     0.0308   0.004707        152        640: 1\n",
      "tensor([0.91446], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.735      0.592      0.651      0.378\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/99      3.53G      0.035    0.03071   0.004895        136        640: 1\n",
      "tensor([0.91276], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.793       0.58      0.659      0.356\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/99      3.53G    0.03471    0.03033   0.004563        134        640: 1\n",
      "tensor([0.86961], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.794      0.646      0.719       0.42\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/99      3.53G     0.0342    0.02959   0.004404        182        640: 1\n",
      "tensor([0.93129], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.734      0.595      0.645      0.362\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/99      3.53G    0.03378    0.02976   0.004188        128        640: 1\n",
      "tensor([0.78854], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.88      0.617      0.738      0.424\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/99      3.53G    0.03335    0.02901    0.00406        112        640: 1\n",
      "tensor([0.86430], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.818      0.623      0.724      0.412\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/99      3.53G    0.03345    0.02928   0.004058        151        640: 1\n",
      "tensor([0.84174], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.793      0.602      0.688      0.382\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/99      3.53G    0.03325    0.02927   0.004036        132        640: 1\n",
      "tensor([0.87194], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.771      0.667      0.732      0.433\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/99      3.53G    0.03287    0.02879   0.003914        131        640: 1\n",
      "tensor([0.80985], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.83      0.626       0.71       0.41\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/99      3.53G    0.03296    0.02866   0.003824        159        640: 1\n",
      "tensor([0.96091], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.843      0.597       0.69        0.4\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/99      3.53G    0.03205    0.02827   0.003788        125        640: 1\n",
      "tensor([0.73437], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.792      0.659      0.732      0.417\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/99      3.53G    0.03249    0.02854   0.003723         88        640: 1\n",
      "tensor([0.69154], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.847      0.657      0.751      0.436\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/99      3.53G    0.03214    0.02782   0.003733        137        640: 1\n",
      "tensor([0.89657], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.823      0.636      0.724      0.424\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/99      3.53G    0.03157    0.02812   0.003591        166        640: 1\n",
      "tensor([0.94232], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.801      0.628       0.71      0.412\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/99      3.53G    0.03151    0.02779   0.003511        161        640: 1\n",
      "tensor([0.85896], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.82      0.612      0.703      0.397\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/99      3.53G    0.03153    0.02734    0.00349        118        640: 1\n",
      "tensor([0.74711], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.848      0.641      0.738      0.435\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/99      3.53G    0.03097    0.02736   0.003394        151        640: 1\n",
      "tensor([0.82887], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.829      0.639      0.727      0.425\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/99      3.53G    0.03132    0.02771   0.003462        133        640: 1\n",
      "tensor([0.81436], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.89      0.612      0.723      0.422\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/99      3.53G    0.03103    0.02738   0.003388        154        640: 1\n",
      "tensor([0.93280], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.851      0.609       0.71      0.409\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/99      3.53G     0.0312    0.02751   0.003456        122        640: 1\n",
      "tensor([0.76180], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.872      0.637      0.737      0.436\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/99      3.53G    0.03069    0.02718   0.003232        123        640: 1\n",
      "tensor([0.64490], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.81      0.636      0.708      0.417\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/99      3.53G    0.03061    0.02685   0.003233        127        640: 1\n",
      "tensor([0.68125], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.88      0.543      0.667      0.395\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/99      3.53G    0.03017    0.02604   0.003209        127        640: 1\n",
      "tensor([0.70543], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.839      0.614       0.71      0.425\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/99      3.53G    0.03007    0.02651   0.003251        122        640: 1\n",
      "tensor([0.76428], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.834      0.603      0.686      0.411\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/99      3.53G    0.03037    0.02683    0.00309        146        640: 1\n",
      "tensor([0.84047], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.797      0.626      0.711      0.412\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/99      3.53G    0.02972    0.02609   0.003112        202        640: 1\n",
      "tensor([0.94248], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675        0.9      0.582      0.709      0.422\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/99      3.53G     0.0299    0.02616   0.003172         94        640: 1\n",
      "tensor([0.64339], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.872      0.603       0.71       0.43\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/99      3.53G    0.02964    0.02607   0.003047        152        640: 1\n",
      "tensor([0.85638], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.846      0.641      0.722      0.439\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/99      3.53G    0.02952    0.02588   0.003061        123        640: 1\n",
      "tensor([0.69029], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.827      0.637      0.728       0.44\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      38/99      3.53G    0.02939    0.02624   0.003097        161        640: 1\n",
      "tensor([0.77945], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.87      0.633      0.733      0.442\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      39/99      3.53G     0.0294    0.02614   0.003033        122        640: 1\n",
      "tensor([0.68522], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.916      0.572      0.713      0.421\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      40/99      3.53G     0.0291    0.02571   0.003038        126        640: 1\n",
      "tensor([0.66695], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.875      0.573       0.71      0.425\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      41/99      3.53G    0.02889    0.02571   0.002987         90        640: 1\n",
      "tensor([0.62596], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.842      0.566      0.686      0.416\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      42/99      3.53G    0.02873    0.02543   0.002863        118        640: 1\n",
      "tensor([0.71323], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.836      0.607       0.71      0.418\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      43/99      3.53G    0.02867    0.02549   0.002751        157        640: 1\n",
      "tensor([0.79344], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.862      0.606      0.706      0.427\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      44/99      3.53G    0.02871    0.02547    0.00283        104        640: 1\n",
      "tensor([0.58180], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.885      0.598       0.72      0.437\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      45/99      3.53G    0.02851    0.02571     0.0027        157        640: 1\n",
      "tensor([0.75248], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.795      0.607      0.697      0.424\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      46/99      3.53G    0.02844    0.02504    0.00273        108        640: 1\n",
      "tensor([0.56217], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.857      0.597      0.693      0.422\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      48/99      3.53G    0.02799      0.025   0.002723        118        640: 1\n",
      "tensor([0.69022], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.878       0.59      0.707      0.428\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      49/99      3.53G    0.02817    0.02526   0.002725        176        640: 1\n",
      "tensor([0.87978], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.861       0.62      0.727      0.448\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      50/99      3.53G    0.02815    0.02494   0.002758        130        640: 1\n",
      "tensor([0.68495], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.877      0.591      0.714      0.444\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      51/99      3.53G    0.02756    0.02493   0.002586        178        640: 1\n",
      "tensor([0.88371], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.855      0.648      0.738      0.464\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      52/99      3.53G     0.0275    0.02459   0.002649        148        640: 1\n",
      "tensor([0.69646], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.802      0.624      0.698      0.428\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      53/99      3.53G    0.02763    0.02448   0.002553        115        640: 1\n",
      "tensor([0.66279], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.841      0.642      0.736       0.45\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      54/99      3.53G    0.02755    0.02435    0.00262        124        640: 1\n",
      "tensor([0.65752], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.858      0.606      0.705      0.437\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      55/99      3.53G    0.02714    0.02406   0.002616        163        640: 1\n",
      "tensor([0.70188], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.845      0.599      0.707      0.426\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      56/99      3.53G    0.02713    0.02438    0.00256        200        640: 1\n",
      "tensor([0.78310], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.831      0.637      0.719      0.434\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      57/99      3.53G    0.02721     0.0244   0.002561        141        640: 1\n",
      "tensor([0.68403], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.87      0.652      0.746      0.445\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      58/99      3.53G    0.02711    0.02423     0.0026        146        640: 1\n",
      "tensor([0.68082], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.848      0.633       0.73      0.442\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      59/99      3.53G    0.02695    0.02413   0.002508        168        640: 1\n",
      "tensor([0.70786], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.881      0.632      0.736      0.455\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      60/99      3.53G    0.02687    0.02388   0.002423        175        640: 1\n",
      "tensor([0.76435], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.828      0.641      0.724      0.434\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      61/99      3.53G    0.02662    0.02404    0.00253        139        640: 1\n",
      "tensor([0.77570], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.846      0.586      0.694      0.417\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      62/99      3.53G    0.02651    0.02319   0.002419        117        640: 1\n",
      "tensor([0.63057], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.892      0.582        0.7      0.429\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      63/99      3.53G    0.02656    0.02352   0.002363        129        640: 1\n",
      "tensor([0.67593], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.853      0.591       0.69      0.419\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      64/99      3.53G    0.02642    0.02365   0.002476        109        640: 1\n",
      "tensor([0.61697], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.888      0.591      0.712      0.431\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      65/99      3.53G    0.02644    0.02376   0.002337        154        640: 1\n",
      "tensor([0.78986], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.857      0.625      0.725      0.431\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      66/99      3.53G     0.0261    0.02348   0.002404        119        640: 1\n",
      "tensor([0.64393], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.829      0.652      0.725       0.44\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      67/99      3.53G    0.02606    0.02293    0.00236        153        640: 1\n",
      "tensor([0.70614], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.869      0.621      0.709      0.425\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      68/99      3.53G    0.02607    0.02341   0.002361        116        640: 1\n",
      "tensor([0.60444], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.876      0.614       0.71      0.432\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      69/99      3.53G    0.02566    0.02275   0.002365        141        640: 1\n",
      "tensor([0.69524], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.878      0.598      0.709      0.436\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      70/99      3.53G    0.02577    0.02304    0.00231        175        640: 1\n",
      "tensor([0.82561], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.882      0.602      0.708      0.434\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      71/99      3.53G    0.02586    0.02302   0.002316        161        640: 1\n",
      "tensor([0.72294], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.873      0.594      0.699      0.418\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      72/99      3.53G    0.02566    0.02271   0.002231        114        640: 1\n",
      "tensor([0.63169], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.887      0.615       0.71      0.437\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      73/99      3.53G    0.02579    0.02321   0.002341        141        640: 1\n",
      "tensor([0.70560], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.877      0.611      0.698      0.427\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      74/99      3.53G    0.02561    0.02305   0.002235        133        640: 1\n",
      "tensor([0.59594], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.875      0.614      0.708      0.431\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      75/99      3.53G     0.0253    0.02252   0.002216        159        640: 1\n",
      "tensor([0.75580], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.832       0.63       0.72      0.441\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      76/99      3.53G    0.02551    0.02256    0.00226        122        640: 1\n",
      "tensor([0.56915], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.877      0.628       0.72      0.443\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      77/99      3.53G    0.02544    0.02284   0.002239        137        640: 1\n",
      "tensor([0.66711], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.876      0.631      0.713       0.45\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      78/99      3.53G    0.02534    0.02235   0.002224        137        640: 1\n",
      "tensor([0.66894], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.895      0.613      0.709      0.433\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      79/99      3.53G    0.02479     0.0219   0.002253        161        640: 1\n",
      "tensor([0.72546], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.867      0.621      0.715      0.444\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      80/99      3.53G    0.02502    0.02241   0.002148        154        640: 1\n",
      "tensor([0.62818], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.807      0.653      0.717      0.445\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      81/99      3.53G    0.02487    0.02208   0.002151        181        640: 1\n",
      "tensor([0.74626], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.889      0.619      0.722      0.452\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      82/99      3.53G    0.02472      0.022   0.002139        149        640: 1\n",
      "tensor([0.61538], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.889      0.628      0.723      0.449\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      83/99      3.53G     0.0247    0.02206   0.002075        118        640: 1\n",
      "tensor([0.59344], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.89      0.622      0.728      0.457\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      84/99      3.53G    0.02458    0.02189   0.002111        178        640: 1\n",
      "tensor([0.74581], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.884      0.617      0.723      0.449\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      85/99      3.53G    0.02448    0.02187   0.002077        140        640: 1\n",
      "tensor([0.64139], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.887      0.629       0.73      0.455\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      86/99      3.53G     0.0245    0.02191   0.002114        119        640: 1\n",
      "tensor([0.51741], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.872      0.639      0.729      0.454\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      87/99      3.53G    0.02442    0.02204   0.002028        114        640: 1\n",
      "tensor([0.50692], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.848      0.659      0.736       0.45\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      88/99      3.53G     0.0245    0.02168   0.002102        117        640: 1\n",
      "tensor([0.54575], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.896      0.615      0.721      0.446\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      89/99      3.53G    0.02436    0.02179   0.002009        118        640: 1\n",
      "tensor([0.55873], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.882      0.618      0.714      0.444\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      90/99      3.53G    0.02422    0.02149   0.001974        115        640: 1\n",
      "tensor([0.57880], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.893      0.609      0.717      0.441\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      91/99      3.53G    0.02398    0.02123   0.001983        159        640: 1\n",
      "tensor([0.73064], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.848      0.646       0.72      0.448\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      92/99      3.53G      0.024    0.02146   0.002045        165        640: 1\n",
      "tensor([0.68908], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.886      0.628      0.719      0.448\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      93/99      3.53G    0.02387    0.02112   0.002006        126        640: 1\n",
      "tensor([0.56315], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.885       0.63      0.726      0.452\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      94/99      3.53G    0.02382    0.02116   0.002026        112        640: 1\n",
      "tensor([0.57619], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.878      0.618      0.716      0.448\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      95/99      3.53G    0.02361      0.021     0.0019        121        640: 1\n",
      "tensor([0.58600], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.891      0.597      0.707      0.445\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      96/99      3.53G    0.02386    0.02093   0.001991        195        640: 1\n",
      "tensor([0.63334], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.871      0.619      0.717      0.444\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      97/99      3.53G    0.02385    0.02107   0.001957        101        640: 1\n",
      "tensor([0.59191], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.875      0.636      0.722      0.448\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      98/99      3.53G    0.02356    0.02092   0.001993        137        640: 1\n",
      "tensor([0.56588], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.881      0.626       0.72      0.452\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      99/99      3.53G    0.02362    0.02104     0.0019        115        640: 1\n",
      "tensor([0.48818], device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.887      0.632      0.721      0.456\n",
      "\n",
      "100 epochs completed in 1.498 hours.\n",
      "Optimizer stripped from runs/train/exp81/weights/last.pt, 14.3MB\n",
      "Optimizer stripped from runs/train/exp81/weights/best.pt, 14.3MB\n",
      "\n",
      "Validating runs/train/exp81/weights/best.pt...\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.855      0.647      0.737      0.464\n",
      "                   Car       1048       4012      0.909      0.818      0.904      0.647\n",
      "                   Van       1048        431      0.843      0.712      0.805      0.566\n",
      "                 Truck       1048        166      0.939      0.743      0.853      0.593\n",
      "                  Tram       1048         56      0.866      0.661       0.76      0.481\n",
      "            Pedestrian       1048        618       0.76      0.573      0.656      0.322\n",
      "        Person_sitting       1048         20      0.843        0.6      0.664      0.366\n",
      "               Cyclist       1048        234      0.872      0.534      0.619      0.338\n",
      "                  Misc       1048        138      0.805      0.538      0.638      0.397\n",
      "Results saved to \u001b[1mruns/train/exp81\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : exp\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/yolov5/c179ff0be2584b6fa5204027221ec893\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_f1                         : 0.8608007099319769\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_false_positives            : 329.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_mAP@.5                     : 0.9039418198673224\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_mAP@.5:.95                 : 0.6469914413251951\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_precision                  : 0.9088426093198135\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_recall                     : 0.8175828495494497\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_support                    : 4012\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_true_positives             : 3280.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_f1                     : 0.6625414403103236\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_false_positives        : 18.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_mAP@.5                 : 0.6185154470546927\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_mAP@.5:.95             : 0.3378636487855865\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_precision              : 0.8720835657970162\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_recall                 : 0.5341880341880342\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_support                : 234\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_true_positives         : 125.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_f1                        : 0.6451460708733419\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_false_positives           : 18.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_mAP@.5                    : 0.6383990534962232\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_mAP@.5:.95                : 0.39739715346544846\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_precision                 : 0.8049439975196659\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_recall                    : 0.5382853634373036\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_support                   : 138\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_true_positives            : 74.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_f1                  : 0.6530937220512353\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_false_positives     : 112.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_mAP@.5              : 0.6564417497241591\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_mAP@.5:.95          : 0.32172748856143696\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_precision           : 0.759540839375369\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_recall              : 0.5728155339805825\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_support             : 618\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_true_positives      : 354.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_f1              : 0.7011192871797168\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_false_positives : 2.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_mAP@.5          : 0.6641490228173644\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_mAP@.5:.95      : 0.366249404938889\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_precision       : 0.8432307794175499\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_recall          : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_support         : 20\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_true_positives  : 12.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_f1                        : 0.7493829607690409\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_false_positives           : 6.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_mAP@.5                    : 0.7597135010650845\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_mAP@.5:.95                : 0.4807234880912213\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_precision                 : 0.865539422774565\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_recall                    : 0.6607142857142857\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_support                   : 56\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_true_positives            : 37.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_f1                       : 0.8293595896339063\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_false_positives          : 8.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_mAP@.5                   : 0.8527144828269962\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_mAP@.5:.95               : 0.5933107888502399\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_precision                : 0.9390577152497719\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_recall                   : 0.7426099488697707\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_support                  : 166\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_true_positives           : 123.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_f1                         : 0.7720600581574646\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_false_positives            : 57.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_mAP@.5                     : 0.805254808813952\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_mAP@.5:.95                 : 0.566219941250918\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_precision                  : 0.8427700734289123\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_recall                     : 0.7122969837587007\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_support                    : 431\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_true_positives             : 307.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [2610]                    : (0.5314241647720337, 2.199005603790283)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5 [200]          : (0.43239928827192825, 0.7513220377576278)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5:0.95 [200]     : (0.23875434053179195, 0.4639006079891343)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision [200]        : (0.656119409878489, 0.9161416064764574)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall [200]           : (0.3782562679925296, 0.6673630531471584)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [200]           : (0.023564882576465607, 0.03794921189546585)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [200]           : (0.0018999315798282623, 0.007115714251995087)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/obj_loss [200]           : (0.020922640338540077, 0.03434848040342331)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [200]             : (0.03288255259394646, 0.044914670288562775)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [200]             : (0.005345104727894068, 0.01582438126206398)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/obj_loss [200]             : (0.05110734701156616, 0.07565850764513016)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr0 [200]                    : (0.0002980000000000002, 0.07011450381679389)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr1 [200]                    : (0.0002980000000000002, 0.009789529262086514)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr2 [200]                    : (0.0002980000000000002, 0.009789529262086514)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name                        : exp\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Run Path                    : nagasaki-soyorin/yolov5/c179ff0be2584b6fa5204027221ec893\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_batch_metrics     : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_confusion_matrix  : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_per_class_metrics : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_max_image_uploads     : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_mode                  : online\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_model_name            : yolov5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams             : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_enable           : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_lambda           : 10.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_pt               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     anchor_t            : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     artifact_alias      : latest\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch_size          : 16\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box                 : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bucket              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cache               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls                 : 0.05000000000000001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees             : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     entity              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve_population   : data/hyps\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok            : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fl_gamma            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr              : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     freeze              : [0]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h               : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s               : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v               : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|anchor_t        : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|box             : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls             : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|copy_paste      : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|degrees         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fl_gamma        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fliplr          : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|flipud          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_h           : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_s           : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_v           : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|iou_t           : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lr0             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lrf             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mixup           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|momentum        : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mosaic          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj             : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|perspective     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|scale           : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|shear           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|translate       : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_bias_lr  : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_epochs   : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_momentum : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|weight_decay    : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     image_weights       : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz               : 640\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou_t               : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     local_rank          : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum            : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     multi_scale         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                : exp\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_console      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_file         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noautoanchor        : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noplots             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nosave              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noval               : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj                 : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer           : SGD\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience            : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project             : runs/train\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     quad                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume_evolve       : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir            : runs/train/exp81\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period         : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale               : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed                : 0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sync_bn             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate           : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     upload_dataset      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_conf_threshold  : 0.001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_iou_threshold   : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr      : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs       : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum     : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay        : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers             : 8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset                        : 13 (1.93 MB)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-environment-definition : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-info                   : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-specification          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix             : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     git metadata                 : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images                       : 106\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages           : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model graph                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n"
     ]
    }
   ],
   "source": [
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_SI.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_kitti.yaml \\\n",
    "--data data/kitti.yaml \\\n",
    "--epochs 100 \\\n",
    "--weights ./runs/train/fog_02/weights/best.pt \\\n",
    "--ewc_pt runs/train/fog_02/weights/fisher.pt \\\n",
    "--ewc_lambda 5e-4 \\\n",
    "\"\"\"\n",
    "!{command}\n",
    "# --weights ./runs/train/exp3/weights/best.pt \\\n",
    "# L2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a4d00b9-b723-45ba-878c-cef64f438fb6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "976d5ea6-7482-4f40-b8e2-212b9075d2ed",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "190ac993-7cff-4ea4-b4ff-a8c15a68c0a3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f3ee59f-a389-4bf2-9e0e-05cc19103eab",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92db3ab4-0284-44a4-bd09-434b1afde6f5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "147ae885-2ee6-4087-9145-11136a9f6023",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti.yaml, weights=['runs/train/exp81/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 cbe9b398 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.883      0.613      0.735      0.456\n",
      "                   Car       2244       8711      0.931      0.758      0.887      0.637\n",
      "                   Van       2244        861      0.882      0.614      0.754      0.515\n",
      "                 Truck       2244        333      0.965      0.739      0.868      0.643\n",
      "                  Tram       2244        138      0.962      0.659        0.8      0.459\n",
      "            Pedestrian       2244       1286      0.801      0.587      0.676      0.353\n",
      "        Person_sitting       2244         89      0.709      0.551      0.633      0.309\n",
      "               Cyclist       2244        496       0.95      0.422      0.575      0.321\n",
      "                  Misc       2244        284      0.865      0.577      0.682      0.414\n",
      "Speed: 0.0ms pre-process, 0.9ms inference, 1.6ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp76\u001b[0m\n",
      "Test set val successfully!\n"
     ]
    }
   ],
   "source": [
    "model = f'runs/train/exp81/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successfully!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n",
    "# 这个是没有ewc\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22299610-6d58-4212-8fa2-2c176ab5515e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b20397e5-24a5-48f6-beae-182ea7977408",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ab2dcb3-c14e-4f2d-92bd-a2f1cc99404f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 目前最好ewc_lambda 1e-3 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e2b6fd1-dbfa-438f-9057-62f192ea8006",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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